The main goal of AutoML is to make the machine learning model development more accessible. In automated machine learning, you don’t need data scientists with vast ML experience to supervise the machine learning process. However, you need to know what it is you want the model to do.
What is automated machine learning?
AutoML, or automatic machine learning, is an approach to designing machine learning systems that minimise human involvement in the processes of selection, configuration and training of machine learning models. Automated machine learning tools can be used both by machine learning professionals who want to make the modeling process faster and easier, and by people without specialised machine and deep learning experience who want to use machine learning in their projects.
AutoML can encompass practically every stage of building a machine learning model: from a raw dataset, through selecting and adjusting machine learning algorithms, to a model that is ready for deployment.
How to create machine learning models?
The process allows developers with limited machine learning experience to train high-quality models tailored to their business needs. Build your own custom machine learning model in few minutes.
There is a variety of AutoML tools available on the market, both open-source and commercial, that offer varying levels of automation and customisation to meet different user needs.
Here is a breakdown of what automated machine learning can do:
Feature selection (Feature Engineering)
Feature engineering is the core of a data scientist’s job. It is now a matter of importing raw data and allowing to AutoML process to automatically select and transform input data features to improve model performance.
Different datasets require different models. Choosing the right one is not an easy task, unless you are an experienced data scientist. AutoML selects the appropriate machine learning algorithm or model architecture based on data characteristics.
Even when the model is selected, the process of tweaking it to fit our purposes can be gruelling. Automated machine learning (AutoML) can easily optimise model hyperparameters such as learning rates and tree depth for best performance.
Even the best possible model needs to be trained. AutoML automatically trains the generated models with the training data. This taks is usually one of the most time consuming for data scientists.
Once the machine learning model is ready, it’s time to evaluate its performance. AutoML can perform automatic assessment of model quality based on a set of evaluation metrics.
What are the benefits of using AutoML on Google Cloud?
Here are some of the main benefits of using automatic machine learning in Google Cloud:
Easy to use
AutoML on Google Cloud is designed to be accessible to people without advanced machine learning experience. User interfaces are intuitive and the model creation process is automated.
Fast to implement
With automatic machine learning, you can significantly speed up the process of creating machine models. There is no need to spend a lot of time manually adjusting hyperparameters, selecting the best features or testing different models.
Optimise hyperparameters automatically
AutoML on Google Cloud offers automatic hyperparameter optimisation, allowing you to achieve better results without having to manually adjust model parameters.
Supports various types of data
AutoML in Google Cloud supports various types of data, such as images, text, audio, and tabular data. It also offers specialised solutions for specific applications, e.g. AutoML Vision AI, AutoML Natural Language or AutoML Tables.
Flexible and scalable
Thanks to Google Cloud, you can easily adapt computing resources to the needs of your project. You can adjust the amount of processing power as your project requirements increase.
Easy to integrate with other Google Cloud services
AutoML connects to other Google cloud services, which allows integration with various tools and solutions available in the cloud.
You can update the model automatically
Google Cloud AutoML enables automatic model updates, keeping them updated to reflect new data and changing business conditions.
Ofers cross-platform support
Models created with AutoML on Google Cloud can be easily deployed across platforms, allowing for flexibility in your choice of runtime environment.
Using AutoML on Google Cloud shortens the project lifecycle of creating machine models and makes machine learning technology more accessible to a wider range of users.
Google Cloud services for AutoML models
The basic tool supporting Google AutoML is Vertex AI, a unified platform that helps build, implement and scale more artificial intelligence models.
- Creating and storing data sets,
- It is Google’s main machine learning tool,
- Vertex AI allows you to experiment and deploy models faster and safer.
- Data-driven decisions
- Optimizing processes
- Fraud detection.
A tool for automatically creating and implementing modern machine learning models operating on structured data (tabular data).
- Support for a wide range of basic tabular data,
- Easy to building models with,
- Easy to implement and scale models.
With AutoML Vision, you can extract data by scanning objects and classifying images in the cloud.
- Uses REST and RPC APIs,
- Image recognition – detects objects (number and location),
- Image classification with custom labels,
- Implements machine learning models.
- Businesses with product images looking to organise their products better and help recommend customers products that fit their preferences best
Similar to AutoML Vision, but in this case based on video content.
- Adding annotations to videos using custom labels,
- Streaming video analysis,
- Shot change detection,
- Track and detect objects in the video.
- Businesses featuring video content that needs to be curated and labelled.
With this tool you can more easily discover the structure and meaning of any text.
- Integration with REST API,
- Custom entity extraction,
- Custom sentiment analysis,
- Handling large data sets.
- Handling corporate documentation (invoices, contracts etc.)
- Sentiment analysis (e.g. Client reviews)
- Content recommendations.
Dynamic detection and translation of various languages.
Features of AutoML Translation:
- Integrated REST and gRPC API interfaces,
- Support for 50 language pairs,
- Translation using non-standard models.
- Translating customer reviews for businesses active in international markets
- Handling client communication in multiple languages.
Who can benefit from Google AutoML?
Google AutoML available as part of Google Cloud solutions brings a number of benefits to companies across industries including:
Online stores will be able to perform more detailed analyses of customer behaviour, making it easier for them to create personalised product recommendations or forecast demand or the level of optimal prices.
For the health sector, medical image analysis can speed up diagnosis, better manage patient data and forecast, among others, localised outbreaks and epidemics.
In the financial industry, automatic machine learning prevents fraud and correctly estimates credit risk.
Manufacturing companies use AutoML to monitor quality or forecast potential equipment failures, as well as optimise production processes and supply chain management.
AutoML also supports education, including personalising the teaching process to the needs of specific students.
Transport and logistics
Optimising delivery routes is difficult to imagine without the help of machine learning models.
Learn more about the Top 5 AI and ML use cases in business.
How much does AutoML cost?
Google AutoML consists of a whole range of solutions. In order to price them correctly, first you need to specify the exact needs that these services should meet.
You can use a certified Google Cloud Partner do an audit and evaluation of your needs and cloud costs. FOTC is supported by cloud architects who will help you prepare a realistic quote, tell you how to optimise your expenses and how to properly configure all the necessary services, contact us.